Kid mortality indicators based on census data in dairy goat herds in the Netherlands


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Kid mortality indicators based on census data in dairy goat herds in the Netherlands


Optimal young stock rearing benefits animal health and welfare and therefore contributes to a more sustainable herd. In order to be able to evaluate the rearing process, producers need insight into their kid rearing results, preferably based on key indicators. Although mortality is considered a useful parameter to assess animal health and welfare, little is known about the prevalence and factors influencing mortality in dairy goat farming. Calculating kid mortality risks may be further challenged by how and when producers record mortality after birth.

A research by GD experts Eveline Dijkstra, Piet Vellema, René van den Brom and Inge Santman-Berends aimed to develop key kid mortality indicators which can support producers in optimising their rearing system. Census data were available from 395 Dutch dairy goat herds from 2016 until and including 2020. Four mortality indicators were defined: mortality risk of neonatal kids, and mortality rates of postnatal, preweaning, and postweaning kids. Mortality percentages were determined for three subgroups of dairy goat herds depending on the quality of available data i.e. accuracy and completeness of data. The quality of mortality data was classified as good in 39% (n=153), fair in 49% (n=192), or poor in 13% (n=50) of the included herds. 

For each of the three quality groups, recorded kid mortality for all four mortality indicators was higher in herds with accurate animal registration. Kid mortality was significantly different (p 0.001) between all quality groups. Other factors affecting kid mortality levels included herd size as the 25% largest herds had lower kid mortality (p=0.005), and a decreasing trend was associated with the age of dairy kids. This study provided insight into kid mortality in Dutch dairy goat herds and showed that it is possible to monitor kid mortality based on routinely collected data. Nevertheless, data quality should be considered when communicating benchmark values and individual results back to farmers. It is therefore recommended that benchmark values are calculated on data from herds with the highest data quality.

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